A new feature extraction method for identification of affected regions and diagnosis of cognitive disorders

Cognitive disorders like AD progressively disintegrate neurons and their interconnections in the brain; thus gradually deteriorating cognitive functions. Automated diagnosis is very important in the early diagnosis of cognitive disorders. Early diagnosis allows in taking measures helping the person to move on. Clinical diagnosis is inefficient as the symptoms start to manifest only after significant atrophy of the cortical structures. This makes management of the conditions difficult. Resent findings have revealed the potential of Neuroimaging as a highly effective tool in the early detection of these disorders as structural changes in the brain set in much before the manifestation of observable symptoms. The disorders are a reflection of degeneration of the cortical structures and hence can be detected by analysis of the structural images of the brain. Therefore, analysis of T1-weighted MRI has become a popular method of early diagnosis of AD. The work proposes a feature extraction method that enables simultaneous identification of the afflicted cortical structures and diagnosis of disorders. The method proposed is based on sparse logistic regression and linear discriminant analysis. The results obtained were better than or comparable with many of the works reported in literature.

[1]  Maximilian Reiser,et al.  Multivariate deformation-based analysis of brain atrophy to predict Alzheimer's disease in mild cognitive impairment , 2007, NeuroImage.

[2]  H. Benali,et al.  Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus. , 2008, Radiology.

[3]  Marie Chupin,et al.  Automatic classi fi cation of patients with Alzheimer ' s disease from structural MRI : A comparison of ten methods using the ADNI database , 2010 .

[4]  Clifford R. Jack,et al.  Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies , 2008, NeuroImage.

[5]  Thomas Strohmer,et al.  Classification of Alzheimer's disease using unsupervised diffusion component analysis. , 2016, Mathematical biosciences and engineering : MBE.

[6]  Christian Callegari,et al.  Advances in Computing, Communications and Informatics (ICACCI) , 2015 .

[7]  D. Louis Collins,et al.  Multivariate analysis of MRI data for Alzheimer's disease, mild cognitive impairment and healthy controls , 2011, NeuroImage.

[8]  P. Mecocci,et al.  An MRI‐based index to measure the severity of Alzheimer's disease‐like structural pattern in subjects with mild cognitive impairment , 2013, Journal of internal medicine.

[9]  E. Carro,et al.  Pathological Alteration in the Choroid Plexus of Alzheimer’s Disease: Implication for New Therapy Approaches , 2012, Front. Pharmacol..

[10]  Jing Li,et al.  Machine Learning Approaches for the Neuroimaging Study of Alzheimer's Disease , 2011, Computer.

[11]  Julie R. Dumont,et al.  Unraveling the contributions of the diencephalon to recognition memory: a review. , 2011, Learning & memory.

[12]  J. Morris,et al.  Current concepts in mild cognitive impairment. , 2001, Archives of neurology.

[13]  J. Pariente,et al.  Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve , 2009, Brain : a journal of neurology.

[14]  H. Benali,et al.  Fully automatic hippocampus segmentation and classification in Alzheimer's disease and mild cognitive impairment applied on data from ADNI , 2009, Hippocampus.